






import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXModel,
		)



class     lowerCamelCase :







		'''simple docstring'''



		def __init__(				self							,						_UpperCamelCase							,						_UpperCamelCase=1_3							,						_UpperCamelCase=7							,						_UpperCamelCase=True							,						_UpperCamelCase=True							,						_UpperCamelCase=True							,						_UpperCamelCase=True							,						_UpperCamelCase=9_9							,						_UpperCamelCase=6_4							,						_UpperCamelCase=5							,						_UpperCamelCase=4							,						_UpperCamelCase=3_7							,						_UpperCamelCase="gelu"							,						_UpperCamelCase=0.1							,						_UpperCamelCase=0.1							,						_UpperCamelCase=5_1_2							,						_UpperCamelCase=1_6							,						_UpperCamelCase=2							,						_UpperCamelCase=0.02							,						_UpperCamelCase=3							,						_UpperCamelCase=4							,						_UpperCamelCase=None							,						)      ->				Optional[Any]:
							UpperCAmelCase_       : Dict				=		parent
							UpperCAmelCase_       : List[str]				=		batch_size
							UpperCAmelCase_       : Union[str, Any]				=		seq_length
							UpperCAmelCase_       : Optional[Any]				=		is_training
							UpperCAmelCase_       : List[str]				=		use_input_mask
							UpperCAmelCase_       : Optional[Any]				=		use_token_type_ids
							UpperCAmelCase_       : Optional[Any]				=		use_labels
							UpperCAmelCase_       : Any				=		vocab_size
							UpperCAmelCase_       : Dict				=		hidden_size
							UpperCAmelCase_       : Union[str, Any]				=		num_hidden_layers
							UpperCAmelCase_       : Any				=		num_attention_heads
							UpperCAmelCase_       : List[str]				=		intermediate_size
							UpperCAmelCase_       : Tuple				=		hidden_act
							UpperCAmelCase_       : Any				=		hidden_dropout_prob
							UpperCAmelCase_       : Optional[Any]				=		attention_probs_dropout_prob
							UpperCAmelCase_       : List[Any]				=		max_position_embeddings
							UpperCAmelCase_       : Tuple				=		type_vocab_size
							UpperCAmelCase_       : Union[str, Any]				=		type_sequence_label_size
							UpperCAmelCase_       : str				=		initializer_range
							UpperCAmelCase_       : Tuple				=		num_labels
							UpperCAmelCase_       : List[str]				=		num_choices
							UpperCAmelCase_       : Any				=		scope
							UpperCAmelCase_       : List[str]				=		vocab_size - 1



		def    __UpperCAmelCase     (				self  )      ->				Optional[int]:
							UpperCAmelCase_       : List[Any]				=		ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size  )

							UpperCAmelCase_       : List[str]				=		None
							if self.use_input_mask:
												UpperCAmelCase_       : Tuple				=		random_attention_mask([self.batch_size, self.seq_length]  )

							UpperCAmelCase_       : Any				=		None
							if self.use_labels:
												UpperCAmelCase_       : Any				=		ids_tensor([self.batch_size, self.seq_length]							,						self.num_labels  )

							UpperCAmelCase_       : Tuple				=		self.get_config()

							return config, input_ids, input_mask, token_labels



		def    __UpperCAmelCase     (				self  )      ->				str:
							return GPTNeoXConfig(
							    vocab_size=self.vocab_size							,						hidden_size=self.hidden_size							,						num_hidden_layers=self.num_hidden_layers							,						num_attention_heads=self.num_attention_heads							,						intermediate_size=self.intermediate_size							,						hidden_act=self.hidden_act							,						hidden_dropout_prob=self.hidden_dropout_prob							,						attention_probs_dropout_prob=self.attention_probs_dropout_prob							,						max_position_embeddings=self.max_position_embeddings							,						type_vocab_size=self.type_vocab_size							,						is_decoder=_UpperCamelCase							,						initializer_range=self.initializer_range							,						pad_token_id=self.pad_token_id							,						)



		def    __UpperCAmelCase     (				self  )      ->				Any:
							UpperCAmelCase_       : List[str]				=		self.prepare_config_and_inputs()

							UpperCAmelCase_       : str				=		True

							return config, input_ids, input_mask, token_labels



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  )      ->				Any:
							UpperCAmelCase_       : int				=		GPTNeoXModel(config=_UpperCamelCase  )
							model.to(_UpperCamelCase  )
							model.eval()
							UpperCAmelCase_       : Dict				=		model(_UpperCamelCase							,						attention_mask=_UpperCamelCase  )
							UpperCAmelCase_       : Tuple				=		model(_UpperCamelCase  )
							self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size)  )



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  )      ->				Optional[int]:
							UpperCAmelCase_       : List[Any]				=		True
							UpperCAmelCase_       : Tuple				=		GPTNeoXModel(_UpperCamelCase  )
							model.to(_UpperCamelCase  )
							model.eval()
							UpperCAmelCase_       : str				=		model(_UpperCamelCase							,						attention_mask=_UpperCamelCase  )
							self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size)  )



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  )      ->				int:
							UpperCAmelCase_       : Union[str, Any]				=		GPTNeoXForCausalLM(config=_UpperCamelCase  )
							model.to(_UpperCamelCase  )
							model.eval()
							UpperCAmelCase_       : Dict				=		model(_UpperCamelCase							,						attention_mask=_UpperCamelCase							,						labels=_UpperCamelCase  )
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.vocab_size)  )



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  )      ->				Dict:
							UpperCAmelCase_       : int				=		self.num_labels
							UpperCAmelCase_       : str				=		GPTNeoXForQuestionAnswering(_UpperCamelCase  )
							model.to(_UpperCamelCase  )
							model.eval()
							UpperCAmelCase_       : str				=		model(_UpperCamelCase							,						attention_mask=_UpperCamelCase  )
							self.parent.assertEqual(result.start_logits.shape							,						(self.batch_size, self.seq_length)  )
							self.parent.assertEqual(result.end_logits.shape							,						(self.batch_size, self.seq_length)  )



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  )      ->				Optional[Any]:
							UpperCAmelCase_       : str				=		self.num_labels
							UpperCAmelCase_       : List[Any]				=		GPTNeoXForSequenceClassification(_UpperCamelCase  )
							model.to(_UpperCamelCase  )
							model.eval()
							UpperCAmelCase_       : Optional[Any]				=		ids_tensor([self.batch_size]							,						self.type_sequence_label_size  )
							UpperCAmelCase_       : Dict				=		model(_UpperCamelCase							,						attention_mask=_UpperCamelCase							,						labels=_UpperCamelCase  )
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_labels)  )



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  )      ->				Any:
							UpperCAmelCase_       : List[str]				=		self.num_labels
							UpperCAmelCase_       : Union[str, Any]				=		GPTNeoXForTokenClassification(_UpperCamelCase  )
							model.to(_UpperCamelCase  )
							model.eval()
							UpperCAmelCase_       : Optional[Any]				=		model(_UpperCamelCase							,						attention_mask=_UpperCamelCase							,						labels=_UpperCamelCase  )
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.num_labels)  )



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  )      ->				str:
							UpperCAmelCase_       : int				=		True
							UpperCAmelCase_       : Optional[int]				=		GPTNeoXForCausalLM(config=_UpperCamelCase  )
							model.to(_UpperCamelCase  )
							model.eval()

							# first forward pass
							UpperCAmelCase_       : int				=		model(_UpperCamelCase							,						attention_mask=_UpperCamelCase							,						use_cache=_UpperCamelCase  )
							UpperCAmelCase_       : str				=		outputs.past_key_values

							# create hypothetical multiple next token and extent to next_input_ids
							UpperCAmelCase_       : Union[str, Any]				=		ids_tensor((self.batch_size, 3)							,						config.vocab_size  )
							UpperCAmelCase_       : Optional[int]				=		ids_tensor((self.batch_size, 3)							,						vocab_size=2  )

							# append to next input_ids and
							UpperCAmelCase_       : Dict				=		torch.cat([input_ids, next_tokens]							,						dim=-1  )
							UpperCAmelCase_       : List[Any]				=		torch.cat([input_mask, next_mask]							,						dim=-1  )

							UpperCAmelCase_       : List[Any]				=		model(_UpperCamelCase							,						attention_mask=_UpperCamelCase							,						output_hidden_states=_UpperCamelCase  )
							UpperCAmelCase_       : Optional[Any]				=		output_from_no_past["hidden_states"][0]
							UpperCAmelCase_       : Union[str, Any]				=		model(
							    _UpperCamelCase							,						attention_mask=_UpperCamelCase							,						past_key_values=_UpperCamelCase							,						output_hidden_states=_UpperCamelCase							,						)["hidden_states"][0]

							# select random slice
							UpperCAmelCase_       : Dict				=		ids_tensor((1,)							,						output_from_past.shape[-1]  ).item()
							UpperCAmelCase_       : Optional[int]				=		output_from_no_past[:, -3:, random_slice_idx].detach()
							UpperCAmelCase_       : List[Any]				=		output_from_past[:, :, random_slice_idx].detach()

							self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

							# test that outputs are equal for slice
							self.parent.assertTrue(torch.allclose(_UpperCamelCase							,						_UpperCamelCase							,						atol=1E-3  )  )



		def    __UpperCAmelCase     (				self  )      ->				Tuple:
							UpperCAmelCase_       : Optional[int]				=		self.prepare_config_and_inputs()
							UpperCAmelCase_       : str				=		config_and_inputs
							UpperCAmelCase_       : str				=		{"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict





@require_torch
class     lowerCamelCase (__lowercase					,    __lowercase					,    __lowercase					,    unittest.TestCase     ):







		'''simple docstring'''



		_snake_case       :       str					=    (
		    (
		        GPTNeoXModel,
		        GPTNeoXForCausalLM,
		        GPTNeoXForQuestionAnswering,
		        GPTNeoXForSequenceClassification,
		        GPTNeoXForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		_snake_case       :       Any					=    (GPTNeoXForCausalLM,) if is_torch_available() else ()
		_snake_case       :       Optional[int]					=    (
		    {
		        '''feature-extraction''': GPTNeoXModel,
		        '''question-answering''': GPTNeoXForQuestionAnswering,
		        '''text-classification''': GPTNeoXForSequenceClassification,
		        '''text-generation''': GPTNeoXForCausalLM,
		        '''token-classification''': GPTNeoXForTokenClassification,
		        '''zero-shot''': GPTNeoXForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		_snake_case       :       List[Any]					=    False
		_snake_case       :       Union[str, Any]					=    False
		_snake_case       :       int					=    False
		_snake_case       :       Dict					=    False



		def    __UpperCAmelCase     (				self  )      ->				Any:
							UpperCAmelCase_       : Optional[Any]				=		GPTNeoXModelTester(self  )
							UpperCAmelCase_       : int				=		ConfigTester(self							,						config_class=_UpperCamelCase							,						hidden_size=6_4							,						num_attention_heads=8  )



		def    __UpperCAmelCase     (				self  )      ->				Any:
							self.config_tester.run_common_tests()



		def    __UpperCAmelCase     (				self  )      ->				int:
							UpperCAmelCase_       : Any				=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  )



		def    __UpperCAmelCase     (				self  )      ->				str:
							UpperCAmelCase_       : Union[str, Any]				=		self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  )



		def    __UpperCAmelCase     (				self  )      ->				str:
							# This regression test was failing with PyTorch < 1.3
							UpperCAmelCase_       : Tuple				=		self.model_tester.prepare_config_and_inputs_for_decoder()

							UpperCAmelCase_       : int				=		None

							self.model_tester.create_and_check_model_as_decoder(_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  )



		def    __UpperCAmelCase     (				self  )      ->				Optional[int]:
							UpperCAmelCase_       : int				=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  )



		def    __UpperCAmelCase     (				self  )      ->				List[str]:
							UpperCAmelCase_       : Optional[Any]				=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_causal_lm(*_UpperCamelCase  )



		def    __UpperCAmelCase     (				self  )      ->				List[Any]:
							UpperCAmelCase_       : Tuple				=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase  )



		def    __UpperCAmelCase     (				self  )      ->				List[Any]:
							UpperCAmelCase_       : List[str]				=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase  )



		def    __UpperCAmelCase     (				self  )      ->				List[str]:
							UpperCAmelCase_       : Optional[Any]				=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase  )



		@unittest.skip(reason='Feed forward chunking is not implemented'  )
		def    __UpperCAmelCase     (				self  )      ->				Optional[int]:
							pass



		@parameterized.expand([('linear',), ('dynamic',)]  )
		def    __UpperCAmelCase     (				self							,						_UpperCamelCase  )      ->				Union[str, Any]:
							UpperCAmelCase_       : Union[str, Any]				=		self.model_tester.prepare_config_and_inputs_for_common()
							UpperCAmelCase_       : Union[str, Any]				=		ids_tensor([1, 1_0]							,						config.vocab_size  )
							UpperCAmelCase_       : str				=		ids_tensor([1, int(config.max_position_embeddings * 1.5  )]							,						config.vocab_size  )

							set_seed(4_2  )  # Fixed seed at init time so the two models get the same random weights
							UpperCAmelCase_       : int				=		GPTNeoXModel(_UpperCamelCase  )
							original_model.to(_UpperCamelCase  )
							original_model.eval()
							UpperCAmelCase_       : Any				=		original_model(_UpperCamelCase  ).last_hidden_state
							UpperCAmelCase_       : str				=		original_model(_UpperCamelCase  ).last_hidden_state

							set_seed(4_2  )  # Fixed seed at init time so the two models get the same random weights
							UpperCAmelCase_       : Optional[Any]				=		{"type": scaling_type, "factor": 10.0}
							UpperCAmelCase_       : Optional[Any]				=		GPTNeoXModel(_UpperCamelCase  )
							scaled_model.to(_UpperCamelCase  )
							scaled_model.eval()
							UpperCAmelCase_       : Optional[int]				=		scaled_model(_UpperCamelCase  ).last_hidden_state
							UpperCAmelCase_       : Optional[int]				=		scaled_model(_UpperCamelCase  ).last_hidden_state

							# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
							# maximum sequence length, so the outputs for the short input should match.
							if scaling_type == "dynamic":
												self.assertTrue(torch.allclose(_UpperCamelCase							,						_UpperCamelCase							,						atol=1E-5  )  )
							else:
												self.assertFalse(torch.allclose(_UpperCamelCase							,						_UpperCamelCase							,						atol=1E-5  )  )

							# The output should be different for long inputs
							self.assertFalse(torch.allclose(_UpperCamelCase							,						_UpperCamelCase							,						atol=1E-5  )  )





@require_torch
class     lowerCamelCase (unittest.TestCase     ):







		'''simple docstring'''



		@slow
		def    __UpperCAmelCase     (				self  )      ->				Tuple:
							UpperCAmelCase_       : List[Any]				=		AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped'  )
							for checkpointing in [True, False]:
												UpperCAmelCase_       : Dict				=		GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped'  )

												if checkpointing:
																	model.gradient_checkpointing_enable()
												else:
																	model.gradient_checkpointing_disable()
												model.to(_UpperCamelCase  )

												UpperCAmelCase_       : List[str]				=		tokenizer('My favorite food is'							,						return_tensors='pt'  ).to(_UpperCamelCase  )
												# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
												# See: https://github.com/huggingface/transformers/pull/24193
												UpperCAmelCase_       : Union[str, Any]				=		"My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"

												UpperCAmelCase_       : int				=		model.generate(**_UpperCamelCase							,						do_sample=_UpperCamelCase							,						max_new_tokens=2_0  )
												UpperCAmelCase_       : Tuple				=		tokenizer.batch_decode(_UpperCamelCase  )[0]

												self.assertEqual(_UpperCamelCase							,						_UpperCamelCase  )








from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase    :   Union[str, Any]						=			logging.get_logger(__name__)

lowercase    :   Union[str, Any]						=			{
    'snap-research/efficientformer-l1-300': (
        'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
    ),
}




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	'efficientformer'







	def __init__(   self						:Union[str, Any]	,  a						:List[int] = [3, 2, 6, 4]	,  a						:List[int] = [4_8, 9_6, 2_2_4, 4_4_8]	,  a						:List[bool] = [True, True, True, True]	,  a						:int = 4_4_8	,  a						:int = 3_2	,  a						:int = 4	,  a						:int = 7	,  a						:int = 5	,  a						:int = 8	,  a						:int = 4	,  a						:float = 0.0	,  a						:int = 1_6	,  a						:int = 3	,  a						:int = 3	,  a						:int = 3	,  a						:int = 2	,  a						:int = 1	,  a						:float = 0.0	,  a						:int = 1	,  a						:bool = True	,  a						:bool = True	,  a						:float = 1E-5	,  a						:str = "gelu"	,  a						:float = 0.02	,  a						:float = 1E-1_2	,  a						:int = 2_2_4	,  a						:float = 1E-0_5	,  **a						:str	,  )						->					None:
								super().__init__(**a		)

								__UpperCamelCase      :  Optional[Any]       =   hidden_act
								__UpperCamelCase      :  Any       =   hidden_dropout_prob
								__UpperCamelCase      :  str       =   hidden_sizes
								__UpperCamelCase      :  Dict       =   num_hidden_layers
								__UpperCamelCase      :  Optional[Any]       =   num_attention_heads
								__UpperCamelCase      :  str       =   initializer_range
								__UpperCamelCase      :  List[str]       =   layer_norm_eps
								__UpperCamelCase      :  str       =   patch_size
								__UpperCamelCase      :  str       =   num_channels
								__UpperCamelCase      :  Any       =   depths
								__UpperCamelCase      :  Tuple       =   mlp_expansion_ratio
								__UpperCamelCase      :  List[str]       =   downsamples
								__UpperCamelCase      :  Optional[int]       =   dim
								__UpperCamelCase      :  Dict       =   key_dim
								__UpperCamelCase      :  Optional[Any]       =   attention_ratio
								__UpperCamelCase      :  Dict       =   resolution
								__UpperCamelCase      :  Union[str, Any]       =   pool_size
								__UpperCamelCase      :  Tuple       =   downsample_patch_size
								__UpperCamelCase      :  Optional[int]       =   downsample_stride
								__UpperCamelCase      :  Optional[int]       =   downsample_pad
								__UpperCamelCase      :  Union[str, Any]       =   drop_path_rate
								__UpperCamelCase      :  Union[str, Any]       =   num_metaad_blocks
								__UpperCamelCase      :  List[str]       =   distillation
								__UpperCamelCase      :  str       =   use_layer_scale
								__UpperCamelCase      :  Tuple       =   layer_scale_init_value
								__UpperCamelCase      :  str       =   image_size
								__UpperCamelCase      :  int       =   batch_norm_eps